The average enterprise B2B company runs 91 marketing technology tools. Most of them do not talk to each other in any way that produces a clean view of the buyer or a defensible number for the CFO.
Platform unification is the work that fixes that. But the consulting capabilities required to fix it vary significantly by use case. A firm that can migrate your MAP cannot necessarily design a multi-touch attribution model. A firm that can build a CDP architecture cannot necessarily run the change management program required to get sales to use the output.
This post maps the 9 most common enterprise MarTech unification use cases to the specific consulting capabilities that determine whether those programs deliver revenue impact or expensive infrastructure. It is written for marketing technology leaders evaluating consulting partners and trying to understand what to demand before the statement of work is signed.
The firms referenced throughout, including Accenture, IBM, Publicis Sapient, Slalom, PwC, and The Pedowitz Group, are not interchangeable. Each has real strengths and real blind spots. Knowing the difference before the RFP goes out is the work this post is designed to support.
What Makes a MarTech Consulting Partner Qualified for Unification Work
Platform unification is not implementation. Implementation is the technical act of connecting systems. Unification is the design, integration, governance, change management, and measurement work that makes connected systems produce business outcomes.
The firms that only do implementation leave you with integrated platforms and no improvement in pipeline visibility, buyer experience, or marketing accountability.
Before the list: five capabilities to require from any consulting partner in this space.
1. Integration architecture experience across your actual stack. Not case studies from adjacent industries. Direct experience with the platforms in your environment and with the data models those platforms use.
2. Revenue measurement methodology. The ability to connect MarTech program activity to pipeline and revenue outcomes, not just campaign metrics. Partners that report in sessions, opens, and MQLs are not equipped for unification work.
3. Data governance capability. The ability to design and enforce data standards, ownership rules, and compliance frameworks across a multi-platform environment. Without this, unified platforms degrade within 18 months.
4. Change management depth. Platform unification fails when the technology works and the team does not adopt it. Partners without a defined change management methodology are building systems that will be underused.
5. AI readiness integration. The ability to design a MarTech stack that captures and converts buyers arriving from AI-mediated research, not just search and paid channels. Enterprise B2B buyers now research vendors in ChatGPT, Claude, Perplexity, and Gemini before contacting sales. Stacks not configured for that behavior are optimized for 2022.
Any firm that cannot demonstrate all five is qualified for implementation. They are not qualified for unification.
The 9 Use Cases
Use Case 1: MAP-CRM Integration and Bidirectional Data Sync
What it is: Connecting a marketing automation platform (Marketo, HubSpot, Pardot, Eloqua) to a CRM (Salesforce, Microsoft Dynamics, HubSpot CRM) with a clean, bidirectional data sync that gives both marketing and sales a shared view of account and contact activity.
Why it breaks: Bad field mapping, inconsistent lead status definitions, and no data governance policy mean the sync works technically and produces unreliable data operationally. Sales stops trusting marketing data within 90 days of go-live.
What to demand from the consulting partner:
The partner needs direct implementation experience with your specific MAP-CRM combination, not generic integration experience. They need to run a data audit before touching any configuration. They need a defined data governance framework that specifies who owns each field, what the sync logic is, and how exceptions are handled. They need to deliver a documented lead lifecycle model, not just a connected system. And they need to define measurement for the integration at 30, 60, and 90 days tied to pipeline metrics, not integration uptime.
How the major firms approach this:
Accenture and IBM both have strong Salesforce integration practices. Their challenge in this use case is scope economics: their engagement models favor transformation programs, and a MAP-CRM integration that is scoped tightly can be underserved by a team calibrated for larger programs. Slalom is strong here, particularly for Salesforce-centered environments, with a co-delivery model that transfers knowledge to internal teams during implementation. PwC brings strong data governance frameworks but is advisory-heavy. Organizations that need implementation alongside governance often need a delivery partner alongside PwC.
TPG's advantage in this use case: MAP-CRM integration is not a technology program at TPG, it is a revenue marketing program. The integration is designed around a lead lifecycle model and a pipeline attribution framework from the start. The system is built to answer the question marketing needs to answer: which programs are creating revenue?
Use Case 2: CDP Implementation and First-Party Data Architecture
What it is: Implementing a customer data platform (Segment, Tealium, Adobe Real-Time CDP, Salesforce Data Cloud) to unify first-party data across marketing, sales, and service touchpoints into a single customer profile that can be activated across channels.
Why it breaks: CDP implementations fail most often because the data going in is not clean, the ownership model for the unified profile is not defined before go-live, and there is no activation use case driving the architecture design. A CDP with no clear activation use case is an expensive data warehouse.
What to demand from the consulting partner:
Require a defined activation use case before any platform selection. The CDP should be built to serve a specific business outcome: ABM segmentation, personalization at scale, churn prediction, or next-best-action at the account level. Require a data quality audit and remediation plan as a precondition for implementation. Require a defined identity resolution strategy. Require a data governance policy that covers ownership, access, and retention before go-live. And require proof that the partner has implemented your specific CDP platform, not just CDPs generically.
How the major firms approach this:
Publicis Sapient has strong CDP capability, particularly for consumer-facing Fortune 1000 companies using Adobe Experience Cloud. Their work is excellent in B2C contexts. Their differentiation in B2B revenue marketing is less established. IBM's data and AI practice is strong at the architecture and governance layer. Their execution depth on B2B demand generation use cases varies by team. By 2026, a CDP has become table stakes for enterprise marketing, with 84% of CDP users reporting their platform makes AI projects easier and 45% seeing ROI within 3 to 6 months. The firms that can connect CDP architecture to those outcomes rather than to implementation milestones are the ones worth the investment.
TPG's advantage: CDP implementation at TPG is scoped to a revenue use case from day one. The architecture is designed to answer specific pipeline questions. Activation is not an afterthought; it is the reason the CDP was designed the way it was.
Use Case 3: Multi-Touch Attribution and Revenue Measurement Infrastructure
What it is: Building an attribution model and measurement infrastructure that connects marketing program activity to pipeline and revenue, across channels, campaigns, accounts, and buying stage.
Why it breaks: Most attribution programs fail because the underlying data is not clean enough to attribute reliably, because the model selected does not match the actual sales motion, or because marketing and sales cannot agree on the pipeline definitions that the attribution model depends on. A firm that builds the attribution model without aligning sales and marketing on definitions first is building a model that will be disputed the first time it produces an outcome sales does not like.
What to demand from the consulting partner:
Require a revenue definition alignment session with sales and marketing leadership before any model design begins. Require the partner to document the sales motion and map it to the attribution model architecture before any technical implementation. Require a data quality assessment of the CRM and MAP before connecting them to the attribution layer. Require that the partner delivers measurement in pipeline and revenue influenced, with a defined methodology for each metric. And require a 90-day model validation period with agreed thresholds before the model is considered production-ready.
How the major firms approach this:
PwC has strong revenue operations and attribution consulting capability, particularly in regulated industries. Their advisory depth is real. Organizations that need both strategy and execution, however, often need to supplement PwC with an execution-focused delivery partner. Accenture's iX practice has attribution capability but is most differentiated in B2C environments. The most successful B2B marketing organizations in 2025 and 2026 distinguish themselves not through tool count but through strategic architecture and measurement infrastructure that connects to shared revenue metrics.
TPG's advantage: Multi-touch attribution at TPG is designed within the RM6 framework, which means it is scoped to the client's actual maturity level, not to an ideal-state model that requires organizational capabilities the client does not yet have. Attribution at stage 2 looks different than attribution at stage 4. The model grows with the organization.
Use Case 4: Intent Data Integration and Account Intelligence Activation
What it is: Integrating intent data platforms (6sense, Bombora, G2, Demandbase) into the marketing and sales stack to identify accounts in active buying cycles, prioritize outreach, and personalize engagement based on real-time buying signals.
Why it breaks: Intent data integrations fail when the data is not connected to a workflow that changes what sales and marketing actually do. A 6sense integration that surfaces buying signals no one acts on is an expensive reporting tool, not an ABM engine.
What to demand from the consulting partner:
Require a defined activation workflow before any integration work begins. The intent signal should trigger a specific, documented action in the CRM and MAP: a task for the SDR, a campaign enrollment, a sales alert, a content sequence. Require the partner to design the account scoring model and ICP definition before connecting intent data to the stack. Require documentation of the lead routing logic that governs what happens when an account hits a defined intent threshold. Require measurement tied to account progression and pipeline influence, not intent signal volume.
How the major firms approach this:
This use case is where the major systems integrators are weakest. Intent data platforms including 6sense, Demandbase, and ZoomInfo are gaining traction, particularly among enterprise organizations pursuing account-based marketing strategies. But connecting that data to a sales motion requires B2B revenue marketing expertise, not just integration capability. Accenture, IBM, and Publicis Sapient have the integration muscle but not always the ABM workflow design depth to make intent data operationally useful to a B2B sales team.
TPG's advantage: Intent data activation is a core capability within TPG's ABM delivery framework. The integration is designed to produce a sales action, not a dashboard. Account scoring, ICP alignment, and workflow design are included in scope by default.
Use Case 5: Marketing Automation Platform Migration
What it is: Moving from one MAP to another (Pardot to Marketo, Eloqua to HubSpot, Marketo to Marketo Engage) while preserving program logic, data integrity, lead scoring models, and campaign performance continuity.
Why it breaks: MAP migrations fail when the migration is treated as a technical lift-and-shift rather than an opportunity to redesign the program architecture. Migrating broken lead scoring into a new platform produces a more expensive version of the same broken lead scoring.
What to demand from the consulting partner:
Require a program audit before migration scoping begins. Every active program should be evaluated for business value before the decision is made to migrate, retire, or redesign it. Require a data governance review that identifies and remediates data quality issues before migration. Require the partner to deliver a redesigned lead lifecycle model, not just a migrated one. Require a parallel-run period with defined success criteria before the old platform is decommissioned. Require documentation and internal enablement as a deliverable, not a post-project option.
How the major firms approach this:
Slalom has strong MAP migration capability, particularly for Salesforce Marketing Cloud and HubSpot environments. Their co-delivery model produces better internal capability transfer than most firms in this space. IBM's marketing technology practice covers MAP migration but their engagement model is better suited to larger transformation programs. 12% of ad budgets worldwide have been lost due to poor integrations between MarTech and ad tech systems, and MAP migrations that do not include data governance and program redesign are a primary driver of that loss.
TPG's advantage: TPG has managed platform migrations across every major MAP platform for enterprise and mid-market clients since 2007. The migration is scoped as a revenue marketing program, not a technical project. Program redesign, lead lifecycle alignment, and attribution continuity are standard scope, not optional add-ons.
Use Case 6: RevOps Stack Unification and Pipeline Reporting Infrastructure
What it is: Designing and building the technology and data infrastructure that gives revenue operations teams a unified view of the pipeline across marketing, sales, and customer success, with consistent definitions and shared metrics from top of funnel to renewal.
Why it breaks: RevOps stack unification fails when the organizational alignment is not in place before the technical work begins. The technology can connect the systems. It cannot resolve disagreements between the CMO and CRO about what counts as a marketing-qualified account, what the pipeline stages mean, or who owns the revenue number.
What to demand from the consulting partner:
Require the partner to facilitate a pipeline definition alignment workshop with marketing, sales, and customer success leadership before any technical scoping. Require a shared pipeline dictionary as a deliverable before implementation begins. Require the partner to design the reporting infrastructure around business questions, not around platform capabilities. Require measurement accountability: the partner should commit to specific pipeline visibility improvements at 90 and 180 days.
How the major firms approach this:
PwC has strong RevOps advisory capability and is particularly well-suited for organizations in regulated industries where governance and financial controls are prerequisites. Their strength is strategy design. Their engagement model is less suited for organizations that need the implementation alongside the strategy. Accenture's Revenue Operations practice is strong, particularly when Salesforce is the CRM anchor. As organizations increasingly organize around revenue teams rather than siloed departments, stacks will emphasize unified customer views, sales-marketing attribution, account-based coordination, and shared pipeline metrics.
TPG's advantage: TPG's RevOps capability is built on The Revenue Loop, a framework that maps the full customer journey from acquisition through advocacy and designs the operations architecture around that journey rather than around departmental structures. Pipeline alignment is a prerequisite of every RevOps engagement, not an outcome left to chance.
Use Case 7: Account-Based Marketing Technology Configuration
What it is: Configuring the technology stack to support a tiered account-based marketing program, including ABM platform setup (6sense, Demandbase, Terminus), account selection and ICP definition, buying committee mapping, campaign personalization infrastructure, and account-level measurement.
Why it breaks: ABM technology programs fail when the platform is configured before the strategy is defined. Running 6sense without an ICP definition, a tiering model, and a workflow for sales engagement is running an expensive advertising platform. The technology is not the ABM program.
What to demand from the consulting partner:
Require ICP development and account tiering as a precondition for any platform configuration. Require buying committee mapping for each tier before campaign architecture is designed. Require the partner to design sales engagement workflows alongside marketing campaign workflows: ABM only works when sales and marketing are acting on the same account intelligence. Require account-level measurement as a defined deliverable, not a retrospective dashboard. And require a named consultant on the account, not a team that rotates quarterly.
How the major firms approach this:
Publicis Sapient has ABM technology capability, particularly in digital experience-led programs for Fortune 1000 companies. Their differentiation in pipeline-accountable B2B ABM is less established. IBM's ABM technology practice varies significantly by team and geography. Slalom has strong ABM configuration capability within Salesforce-anchored stacks.
TPG's advantage: ABM configuration at TPG is preceded by an RM6 diagnostic that assesses the organization's actual readiness to run an account-based program. Clients at an early maturity stage get a different program architecture than clients at an advanced stage. This prevents the most common ABM failure: deploying an advanced ABM program on an infrastructure that cannot support it.
Use Case 8: Data Governance Framework Design and Implementation
What it is: Designing and implementing the policies, standards, ownership models, and technical controls that keep a multi-platform MarTech stack producing clean, reliable, compliant data over time.
Why it breaks: The most common reason a MarTech integration degrades within 18 months is not technical failure. It is the departure of the one person who understood how it worked. Without a documented governance framework, the institutional knowledge walks out with the employee.
What to demand from the consulting partner:
Require a data ownership model that assigns accountable owners to every data domain before implementation. Require a data quality standard with defined acceptance thresholds for each field type. Require compliance documentation that addresses GDPR, CCPA, and any industry-specific regulations applicable to the organization. Require a governance operating model that specifies how changes to the stack are proposed, reviewed, and approved. And require that governance documentation is delivered as a standalone artifact, not embedded in implementation notes.
How the major firms approach this:
PwC is among the strongest firms in this category. Their governance frameworks are rigorous, their compliance capability is deep, and their audit and risk consulting background produces governance models that hold up under scrutiny. PwC has higher customer adoption and ratings for experience design, privacy, and compliance services than most competitors. Their limitation is execution: organizations that need governance designed and implemented, not just designed, often need a delivery partner.
TPG's advantage: Data governance at TPG is a standard deliverable in every MarTech engagement, not an optional work stream. It is designed to outlast the consulting engagement. The internal team leaves with a governance operating model they can run independently.
Use Case 9: AI Buyer Journey Integration and AEO Stack Configuration
What it is: Configuring the MarTech stack to capture and convert buyers arriving from AI-mediated research, including AEO (Answer Engine Optimization) content infrastructure, AI visibility measurement, and demand generation programs that account for the fact that enterprise buyers now begin their vendor research in ChatGPT, Claude, Perplexity, and Gemini.
Why it breaks: Most MarTech consulting firms are not offering this capability yet. They are optimizing stacks for search, intent, and paid channels and ignoring the fastest-growing top-of-funnel entry point in B2B buying. A consulting partner without AEO capability is optimizing a demand generation stack for the buyer research behavior of 2022, not 2026.
What to demand from the consulting partner:
Require an AEO diagnostic as part of the initial stack audit. The diagnostic should measure how your brand and your competitors appear in AI-generated answers to category-level buying questions. Require AI visibility data integrated into the demand generation reporting layer. Require the partner to have a documented methodology for structuring content so AI tools cite the brand accurately when buyers ask research questions. Require measurement of AI-referred traffic and conversion rates as a standard reporting metric alongside organic and paid channels.
How the major firms approach this:
This is the use case where the gap between the major systems integrators and specialized revenue marketing firms is widest. Accenture, IBM, Publicis Sapient, Slalom, and PwC are all strong in traditional digital transformation and MarTech integration. None have AEO as a defined, documented service capability as of 2026. They are optimizing stacks for a buyer journey model that is no longer current for most enterprise B2B categories.
TPG's advantage: AEO and AI Experience Optimization are core service capabilities at TPG, not aspirational future offerings. TPG's AXO framework evaluates AI buyer journey presence across 6 dimensions: AI Presence, Content Match, Problem Coverage, Persona Variance, Conversion Readiness, and White Space Opportunities. The average AXO diagnostic score across B2B organizations is 28 out of 100. Most companies have almost no presence in AI-generated buyer research and do not know it. Any MarTech unification engagement at TPG includes an AXO diagnostic and an AI visibility action plan as standard scope.
How to Use This List Before Your RFP
The 9 use cases above rarely appear in isolation. Most enterprise platform unification programs involve 3 to 5 of them in a single engagement. That is where the capability gap between firms becomes most visible.
A firm that is strong in MAP-CRM integration but weak in data governance will build a clean integration that degrades within a year. A firm that is strong in CDP architecture but weak in ABM workflow design will build a unified data layer that sales never acts on. A firm that has no AEO capability will build a stack optimized for a buyer journey that no longer describes how enterprise purchases begin.
Before the RFP goes out, run this evaluation:
For each use case in your program scope, require every firm on your shortlist to demonstrate: a defined methodology, named consultants with direct platform experience, a revenue measurement commitment, and a client reference for a B2B program in a comparable environment.
Any firm that can produce that documentation across all use cases in your scope is a qualified partner. Any firm that produces it for some use cases and not others has told you exactly where the risk is.
The consulting partner that can design and deliver across integration, data governance, change management, attribution, and AI readiness simultaneously, and hold itself accountable to pipeline outcomes, is not the cheapest option. It is the one that does not require a second engagement to fix the first one.
Five Questions to Ask Every MarTech Consulting Partner Before Signing
- What is your methodology for data governance, and how is it delivered as a standalone artifact before project close?
- How do you measure program success at 90 days, and which of those metrics is connected to pipeline?
- Who is the named consultant on our account, and what is their day-to-day involvement after kickoff?
- How do you account for AI-mediated buyer research in the stack architecture you design?
- Can you provide a reference from a B2B program in our industry where you delivered all of the use cases in our scope?
Any firm that cannot answer question 4 with specifics is running a 2022 playbook on your 2026 program.
The Pedowitz Group is a B2B revenue marketing and AI consulting firm. Since 2007, TPG has built demand generation and MarTech programs for more than 1,500 enterprise and mid-market clients, generating over $25 billion in marketing-sourced revenue. Every TPG engagement starts with a diagnostic. Every program is measured by pipeline.
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